The Automatic Identification System (AIS) allows vessels to share identification, characteristics, and location data through self-reporting. This information is periodically broadcast and can be received by other vessels with AIS transceivers, as well as ground or satellite sensors. Since the International Maritime Organisation (IMO) mandated AIS for vessels above 300 gross tonnage, extensive datasets have emerged, becoming a valuable resource for maritime intelligence.

Maritime collisions occur when two vessels collide or when a vessel collides with a floating or stationary object, such as an iceberg. Maritime collisions hold significant importance in the realm of marine accidents for several reasons:

Injuries and fatalities of vessel crew members and passengers.

Environmental effects, especially in cases involving large tanker ships and oil spills.

Direct and indirect economic losses on local communities near the accident area.

Adverse financial consequences for ship owners, insurance companies and cargo owners including vessel loss and penalties.

As sea routes become more congested and vessel speeds increase, the likelihood of significant accidents during a ship's operational life rises. The increasing congestion on sea lanes elevates the probability of accidents and especially collisions between vessels.

The development of solutions and models for the analysis, early detection and mitigation of vessel collision events is a significant step towards ensuring future maritime safety. In this context, a synthetic vessel proximity event dataset is created using real vessel AIS messages. The synthetic dataset of trajectories with reconstructed timestamps is generated so that a pair of trajectories reach simultaneously their intersection point, simulating an unintended proximity event (collision close call). The dataset aims to provide a basis for the development of methods for the detection and mitigation of maritime collisions and proximity events, as well as the study and training of vessel crews in simulator environments.

The dataset consists of 4658 samples/AIS messages of 213 unique vessels from the Aegean Sea. The steps that were followed to create the collision dataset are:

Given 2 vessels X (vessel_id1) and Y (vessel_id2) with their current known location (LATITUDE [lat], LONGITUDE [lon]):

Check if the trajectories of vessels X and Y are spatially intersecting.

If the trajectories of vessels X and Y are intersecting, then align temporally the timestamp of vessel Y at the intersect point according to X’s timestamp at the intersect point. The temporal alignment is performed so the spatial intersection (nearest proximity point) occurs at the same time for both vessels.

Also for each vessel pair the timestamp of the proximity event is different from a proximity event that occurs later so that different vessel trajectory pairs do not overlap temporarily.

Two csv files are provided. vessel_positions.csv includes the AIS positions vessel_id, t, lon, lat, heading, course, speed of all vessels. Simulated_vessel_proximity_events.csv includes the id, position and timestamp of each identified proximity event along with the vessel_id number of the associated vessels. The final sum of unintended proximity events in the dataset is 237. Examples of unintended vessel proximity events are visualized in the respective png and gif files.

The research leading to these results has received funding from the European Union's Horizon Europe Programme under the CREXDATA Project, grant agreement n° 101092749.

Polluting ship accidents is based on reporting of shipping accidents done by HELCOM Contracting Parties to produce annual within HELCOM MARITIME group. Data were available for 2016-2020. Attribute specifications and units Country Year Datedd_m: Date (dd.mm.yyyy) Timehh_m: Time (hh:mm) Latitude: Latitude (decimal degrees) Longitude: Longitude (decimal degrees) Location Ship_1_nam: Ship 1 name, flag Sh1_Categ: Ship 1 type (according to AIS category) Sh1_Type: Details of ship 1 type Sh1_Hull: Hull type (ship 1) Sh1Size_gt: Size (gt) (ship 1) Sh1Sezidwt: Size (dwt) (ship 1) Sh1Draug_m: Draught (m) (ship 1) Ship2_Name: Ship 2 name, flag Sh2_Categ: Shiptype 2 (according to AIS category) Sh2_Type: Details of ship 2 type Sh2_Hull: Hull type (ship 2) Sh1Size_gt: Size (gt) (ship 2) Sh2Sizedwt: Size (dwt) (ship 2) Sh2Draug_m: Draught (m) (ship 2) Cargo_Type Acc_Type Colli_Type Acc_Detail Cause_Sh1 Cause_Sh2 HumanEleme IceCondit CrewIceTra CauseDetail Pilot_Sh1 Pilot_Sh2 Offence Damage Assistance Pollution Pollu_m3 Pollut_t Pollu_Type RespAction Add_Info version F44 original_a original_l original_s original_c

Compared with the young, the elderly (age greater than or equal to 60 years old) vulnerable road users (VRUs) face a greater risk of injury or death in a traffic accident. A contributing vulnerability is the aging processes that affect their brain structure. The purpose of this study was to investigate the injury mechanisms and establish head AIS 4+ injury tolerances for the elderly VRUs based on various head injury criteria. A total of 30 elderly VRUs accidents with detailed injury records and video information were selected and the VRUs’ kinematics and head injuries were reconstructed by combining a multi-body system model (PC-Crash and MADYMO) and the THUMS (Ver. 4.0.2) FE models. Four head kinematic-based injury predictors (linear acceleration, angular velocity, angular acceleration, and head injury criteria) and three brain tissue injury criteria (coup pressure, maximum principal strain, and cumulative strain damage measure) were studied. The correlation between injury predictors and injury risk was developed using logistical regression models for each criterion. The results show that the calculated thresholds for head injury for the kinematic criteria were lower than those reported in previous literature studies. For the brain tissue level criteria, the thresholds calculated in this study were generally similar to those of previous studies except for the coup pressure. The models had higher (>0.8) area under curve values for receiver operator characteristics, indicating good predictive power. This study could provide additional support for understanding brain injury thresholds in elderly people.

Description of thoracic injuries in the AISThorax≥2 group.

Demographics and mechanism.

The objective of this study was to describe fatal pedestrian injury patterns in youth aged 15 to 24 years old and correlate them with motor vehicle collision (MVC) dynamics and pedestrian kinematics using data from medicolegal death investigations of MVCs occurring in the current Canadian motor vehicle (MV) fleet. Based on a systematic literature review, MVC–pedestrian injuries were collated in an injury data collection form (IDCF). The IDCF was coded using the Abbreviated Injury Scale (AIS) 2015 revision. The AIS of the most frequent severe injury was noted for individual body regions. The Maximum AIS (MAIS) was used to define the most severe injury to the body overall and by body regions (MAISBR). This study focused on serious to maximal injuries (AIS 3–6) that had an increasing likelihood of causing death. The IDCF was used to extract collision and injury data from the Office of the Chief Coroner for Ontario (OCCO) database of postmortem examinations done at the Provincial Forensic Pathology Unit (PFPU) in Toronto, Canada, and other provincial facilities between 2013 and 2019. Injury data were correlated with data about the MVs and MV dynamics and pedestrian kinematics. The study was approved by the Western University Health Science Research Ethics Board (Project ID: 113440; Lawson Health Research Institute Approval No. R-19-066). There were 88 youth, including 54 (61.4%) males and 34 (38.6%) females. Youth pedestrians comprised 13.1% (88/670) of all autopsied pedestrians. Cars (n = 25/88, 28.4%) were the most frequent type of vehicle in single-vehicle impacts, but collectively vehicles with high hood edges (i.e., greater distance between the ground and hood edge) were in the majority. Forward projection (n = 34/88, 38.6%) was the most frequent type of pedestrian kinematics. Regardless of the type of vehicle, there was a tendency in most cases for the median MAISBR ≥ 3 to involve the head and thorax. A similar trend was seen in most of the pedestrian kinematics involving the various frontal impacts. Of the 88 cases, at least 63 (71.6%) were known to be engaged in risk-taking behaviors (e.g., activity on roadway). At least 12 deaths were nonaccidental (8 suicides and 4 homicides). Some activities may have been impairment related, because 26/63 (41.3%) pedestrians undertaking risk-taking behavior on the roadway were impaired. Toxicological analyses revealed that over half of the cases (47/88, 53.4%) tested positive for a drug that could have affected behavior. Ethanol was the most common. Thirty-one had positive blood results. A fatal dyad of head and thorax trauma was observed for pedestrians struck by cars. For those pedestrians hit by vehicles with high hood edges, which were involved in the majority of cases, a fatal triad of injuries to the head, thorax, and abdomen/retroperitoneum was observed. Most deaths occurred from frontal collisions and at speeds more than 35 km/h.